Machine Learning Enables Prediction of Halide Perovskites' Optical Behavior with >90% Accuracy.

ABSTRACT: The composition-dependent degradation of hybrid organic-inorganic perovskites (HOIPs) due to environmental stressors still precludes their commercialization. It is very difficult to quantify their behavior upon exposure to each stressor by exclusively using trial-and-error methods due to the high-dimensional parameter space involved. We implement machine learning (ML) models using high-throughput, in situ photoluminescence (PL) to predict the response of Cs _y FA_1-y Pb(Br _x I_1-x )₃ while exposed to relative humidity cycles. We quantitatively compare three ML models while generating forecasts of environment-dependent PL responses: linear regression, echo state network, and seasonal autoregressive integr

SUBMITTER: Srivastava M 

PROVIDER: S-EPMC10112389 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature